


"""simple docstring"""



from __future__ import annotations

import unittest

import numpy as np

from transformers import LayoutLMConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
		import tensorflow as tf

		from transformers.models.layoutlm.modeling_tf_layoutlm import (
		    TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFLayoutLMForMaskedLM,
		    TFLayoutLMForQuestionAnswering,
		    TFLayoutLMForSequenceClassification,
		    TFLayoutLMForTokenClassification,
		    TFLayoutLMModel,
		)







class    __a						:







				def __init__( self      ,	UpperCAmelCase      ,	UpperCAmelCase=13      ,	UpperCAmelCase=7      ,	UpperCAmelCase=True      ,	UpperCAmelCase=True      ,	UpperCAmelCase=True      ,	UpperCAmelCase=True      ,	UpperCAmelCase=99      ,	UpperCAmelCase=32      ,	UpperCAmelCase=2      ,	UpperCAmelCase=4      ,	UpperCAmelCase=37      ,	UpperCAmelCase="gelu"      ,	UpperCAmelCase=0.1      ,	UpperCAmelCase=0.1      ,	UpperCAmelCase=512      ,	UpperCAmelCase=16      ,	UpperCAmelCase=2      ,	UpperCAmelCase=0.0_2      ,	UpperCAmelCase=3      ,	UpperCAmelCase=4      ,	UpperCAmelCase=None      ,	UpperCAmelCase=1000      ,	):

					'''simple docstring'''






					lowerCAmelCase_      							=			parent
					lowerCAmelCase_      							=			batch_size
					lowerCAmelCase_      							=			seq_length
					lowerCAmelCase_      							=			is_training
					lowerCAmelCase_      							=			use_input_mask
					lowerCAmelCase_      							=			use_token_type_ids
					lowerCAmelCase_      							=			use_labels
					lowerCAmelCase_      							=			vocab_size
					lowerCAmelCase_      							=			hidden_size
					lowerCAmelCase_      							=			num_hidden_layers
					lowerCAmelCase_      							=			num_attention_heads
					lowerCAmelCase_      							=			intermediate_size
					lowerCAmelCase_      							=			hidden_act
					lowerCAmelCase_      							=			hidden_dropout_prob
					lowerCAmelCase_      							=			attention_probs_dropout_prob
					lowerCAmelCase_      							=			max_position_embeddings
					lowerCAmelCase_      							=			type_vocab_size
					lowerCAmelCase_      							=			type_sequence_label_size
					lowerCAmelCase_      							=			initializer_range
					lowerCAmelCase_      							=			num_labels
					lowerCAmelCase_      							=			num_choices
					lowerCAmelCase_      							=			scope
					lowerCAmelCase_      							=			range_bbox







				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					lowerCAmelCase_      							=			ids_tensor([self.batch_size, self.seq_length]      ,	self.vocab_size   )

					# convert bbox to numpy since TF does not support item assignment
					lowerCAmelCase_      							=			ids_tensor([self.batch_size, self.seq_length, 4]      ,	self.range_bbox   ).numpy()
					# Ensure that bbox is legal
					for i in range(bbox.shape[0]   ):
						for j in range(bbox.shape[1]   ):
							if bbox[i, j, 3] < bbox[i, j, 1]:
								lowerCAmelCase_      							=			bbox[i, j, 3]
								lowerCAmelCase_      							=			bbox[i, j, 1]
								lowerCAmelCase_      							=			t
							if bbox[i, j, 2] < bbox[i, j, 0]:
								lowerCAmelCase_      							=			bbox[i, j, 2]
								lowerCAmelCase_      							=			bbox[i, j, 0]
								lowerCAmelCase_      							=			t
					lowerCAmelCase_      							=			tf.convert_to_tensor(UpperCAmelCase   )

					lowerCAmelCase_      							=			None
					if self.use_input_mask:
						lowerCAmelCase_      							=			random_attention_mask([self.batch_size, self.seq_length]   )

					lowerCAmelCase_      							=			None
					if self.use_token_type_ids:
						lowerCAmelCase_      							=			ids_tensor([self.batch_size, self.seq_length]      ,	self.type_vocab_size   )

					lowerCAmelCase_      							=			None
					lowerCAmelCase_      							=			None
					lowerCAmelCase_      							=			None
					if self.use_labels:
						lowerCAmelCase_      							=			ids_tensor([self.batch_size]      ,	self.type_sequence_label_size   )
						lowerCAmelCase_      							=			ids_tensor([self.batch_size, self.seq_length]      ,	self.num_labels   )
						lowerCAmelCase_      							=			ids_tensor([self.batch_size]      ,	self.num_choices   )

					lowerCAmelCase_      							=			LayoutLMConfig(
					    vocab_size=self.vocab_size      ,	hidden_size=self.hidden_size      ,	num_hidden_layers=self.num_hidden_layers      ,	num_attention_heads=self.num_attention_heads      ,	intermediate_size=self.intermediate_size      ,	hidden_act=self.hidden_act      ,	hidden_dropout_prob=self.hidden_dropout_prob      ,	attention_probs_dropout_prob=self.attention_probs_dropout_prob      ,	max_position_embeddings=self.max_position_embeddings      ,	type_vocab_size=self.type_vocab_size      ,	initializer_range=self.initializer_range      ,	)

					return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels







				def      lowerCamelCase_     ( self      ,	UpperCAmelCase      ,	UpperCAmelCase      ,	UpperCAmelCase      ,	UpperCAmelCase      ,	UpperCAmelCase      ,	UpperCAmelCase      ,	UpperCAmelCase      ,	UpperCAmelCase   ):

					'''simple docstring'''






					lowerCAmelCase_      							=			TFLayoutLMModel(config=UpperCAmelCase   )

					lowerCAmelCase_      							=			model(UpperCAmelCase      ,	UpperCAmelCase      ,	attention_mask=UpperCAmelCase      ,	token_type_ids=UpperCAmelCase   )
					lowerCAmelCase_      							=			model(UpperCAmelCase      ,	UpperCAmelCase      ,	token_type_ids=UpperCAmelCase   )
					lowerCAmelCase_      							=			model(UpperCAmelCase      ,	UpperCAmelCase   )
					self.parent.assertEqual(result.last_hidden_state.shape      ,	(self.batch_size, self.seq_length, self.hidden_size)   )
					self.parent.assertEqual(result.pooler_output.shape      ,	(self.batch_size, self.hidden_size)   )







				def      lowerCamelCase_     ( self      ,	UpperCAmelCase      ,	UpperCAmelCase      ,	UpperCAmelCase      ,	UpperCAmelCase      ,	UpperCAmelCase      ,	UpperCAmelCase      ,	UpperCAmelCase      ,	UpperCAmelCase   ):

					'''simple docstring'''






					lowerCAmelCase_      							=			TFLayoutLMForMaskedLM(config=UpperCAmelCase   )

					lowerCAmelCase_      							=			model(UpperCAmelCase      ,	UpperCAmelCase      ,	attention_mask=UpperCAmelCase      ,	token_type_ids=UpperCAmelCase      ,	labels=UpperCAmelCase   )
					self.parent.assertEqual(result.logits.shape      ,	(self.batch_size, self.seq_length, self.vocab_size)   )







				def      lowerCamelCase_     ( self      ,	UpperCAmelCase      ,	UpperCAmelCase      ,	UpperCAmelCase      ,	UpperCAmelCase      ,	UpperCAmelCase      ,	UpperCAmelCase      ,	UpperCAmelCase      ,	UpperCAmelCase   ):

					'''simple docstring'''






					lowerCAmelCase_      							=			self.num_labels
					lowerCAmelCase_      							=			TFLayoutLMForSequenceClassification(config=UpperCAmelCase   )

					lowerCAmelCase_      							=			model(UpperCAmelCase      ,	UpperCAmelCase      ,	attention_mask=UpperCAmelCase      ,	token_type_ids=UpperCAmelCase   )
					self.parent.assertEqual(result.logits.shape      ,	(self.batch_size, self.num_labels)   )







				def      lowerCamelCase_     ( self      ,	UpperCAmelCase      ,	UpperCAmelCase      ,	UpperCAmelCase      ,	UpperCAmelCase      ,	UpperCAmelCase      ,	UpperCAmelCase      ,	UpperCAmelCase      ,	UpperCAmelCase   ):

					'''simple docstring'''






					lowerCAmelCase_      							=			self.num_labels
					lowerCAmelCase_      							=			TFLayoutLMForTokenClassification(config=UpperCAmelCase   )

					lowerCAmelCase_      							=			model(UpperCAmelCase      ,	UpperCAmelCase      ,	attention_mask=UpperCAmelCase      ,	token_type_ids=UpperCAmelCase      ,	labels=UpperCAmelCase   )
					self.parent.assertEqual(result.logits.shape      ,	(self.batch_size, self.seq_length, self.num_labels)   )







				def      lowerCamelCase_     ( self      ,	UpperCAmelCase      ,	UpperCAmelCase      ,	UpperCAmelCase      ,	UpperCAmelCase      ,	UpperCAmelCase      ,	UpperCAmelCase      ,	UpperCAmelCase      ,	UpperCAmelCase   ):

					'''simple docstring'''






					lowerCAmelCase_      							=			TFLayoutLMForQuestionAnswering(config=UpperCAmelCase   )

					lowerCAmelCase_      							=			model(UpperCAmelCase      ,	UpperCAmelCase      ,	attention_mask=UpperCAmelCase      ,	token_type_ids=UpperCAmelCase   )
					self.parent.assertEqual(result.start_logits.shape      ,	(self.batch_size, self.seq_length)   )
					self.parent.assertEqual(result.end_logits.shape      ,	(self.batch_size, self.seq_length)   )







				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					lowerCAmelCase_      							=			self.prepare_config_and_inputs()
					(
					    (
					    lowerCAmelCase_
					)							,	(
					    lowerCAmelCase_
					)							,	(
					    lowerCAmelCase_
					)							,	(
					    lowerCAmelCase_
					)							,	(
					    lowerCAmelCase_
					)							,	(
					    lowerCAmelCase_
					)							,	(
					    lowerCAmelCase_
					)							,	(
					    lowerCAmelCase_
					)							,	
					)      							=			config_and_inputs
					lowerCAmelCase_      							=			{
					    '''input_ids''': input_ids,
					    '''bbox''': bbox,
					    '''token_type_ids''': token_type_ids,
					    '''attention_mask''': input_mask,
					}
					return config, inputs_dict







@require_tf
class    __a						( __snake_case					,			__snake_case					,			unittest.TestCase							):
				lowerCamelCase     :       Tuple			       =(
				    (
				        TFLayoutLMModel,
				        TFLayoutLMForMaskedLM,
				        TFLayoutLMForTokenClassification,
				        TFLayoutLMForSequenceClassification,
				        TFLayoutLMForQuestionAnswering,
				    )
				    if is_tf_available()
				    else ()
				)
				lowerCamelCase     :       Dict			       =(
				    {
				        'feature-extraction': TFLayoutLMModel,
				        'fill-mask': TFLayoutLMForMaskedLM,
				        'text-classification': TFLayoutLMForSequenceClassification,
				        'token-classification': TFLayoutLMForTokenClassification,
				        'zero-shot': TFLayoutLMForSequenceClassification,
				    }
				    if is_tf_available()
				    else {}
				)
				lowerCamelCase     :       Optional[int]			       =False
				lowerCamelCase     :       Any			       =True
				lowerCamelCase     :       Tuple			       =10







				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					lowerCAmelCase_      							=			TFLayoutLMModelTester(self   )
					lowerCAmelCase_      							=			ConfigTester(self      ,	config_class=UpperCAmelCase      ,	hidden_size=37   )







				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					self.config_tester.run_common_tests()







				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					lowerCAmelCase_      							=			self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*UpperCAmelCase   )







				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					lowerCAmelCase_      							=			self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_masked_lm(*UpperCAmelCase   )







				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					lowerCAmelCase_      							=			self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_sequence_classification(*UpperCAmelCase   )







				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					lowerCAmelCase_      							=			self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_token_classification(*UpperCAmelCase   )







				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					lowerCAmelCase_      							=			self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_question_answering(*UpperCAmelCase   )







				@slow
				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					for model_name in TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						lowerCAmelCase_      							=			TFLayoutLMModel.from_pretrained(UpperCAmelCase   )
						self.assertIsNotNone(UpperCAmelCase   )







				@unittest.skip('''Onnx compliancy broke with TF 2.10'''   )
				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					pass




def      UpperCAmelCase     (       )						-> Optional[Any]:


	"""simple docstring"""




	lowerCAmelCase_      							=			tf.convert_to_tensor([[1_0_1,1_0_1_9,1_0_1_4,1_0_1_6,1_0_3_7,1_2_8_4_9,4_7_4_7,1_0_0_4,1_4_2_4_6,2_2_7_8,5_4_3_9,4_5_2_4,5_0_0_2,2_9_3_0,2_1_9_3,2_9_3_0,4_3_4_1,3_2_0_8,1_0_0_5,1_0_5_5,2_1_7_1,2_8_4_8,1_1_3_0_0,3_5_3_1,1_0_2],[1_0_1,4_0_7_0,4_0_3_4,7_0_2_0,1_0_2_4,3_0_5_8,1_0_1_5,1_0_1_3,2_8_6_1,1_0_1_3,6_0_7_0,1_9_2_7_4,2_7_7_2,6_2_0_5,2_7_8_1_4,1_6_1_4_7,1_6_1_4_7,4_3_4_3,2_0_4_7,1_0_2_8_3,1_0_9_6_9,1_4_3_8_9,1_0_1_2,2_3_3_8,1_0_2]]	)  # noqa: E231
	lowerCAmelCase_      							=			tf.convert_to_tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],]	)  # noqa: E231
	lowerCAmelCase_      							=			tf.convert_to_tensor([[[0,0,0,0],[4_2_3,2_3_7,4_4_0,2_5_1],[4_2_7,2_7_2,4_4_1,2_8_7],[4_1_9,1_1_5,4_3_7,1_2_9],[9_6_1,8_8_5,9_9_2,9_1_2],[2_5_6,3_8,3_3_0,5_8],[2_5_6,3_8,3_3_0,5_8],[3_3_6,4_2,3_5_3,5_7],[3_6_0,3_9,4_0_1,5_6],[3_6_0,3_9,4_0_1,5_6],[4_1_1,3_9,4_7_1,5_9],[4_7_9,4_1,5_2_8,5_9],[5_3_3,3_9,6_3_0,6_0],[6_7,1_1_3,1_3_4,1_3_1],[1_4_1,1_1_5,2_0_9,1_3_2],[6_8,1_4_9,1_3_3,1_6_6],[1_4_1,1_4_9,1_8_7,1_6_4],[1_9_5,1_4_8,2_8_7,1_6_5],[1_9_5,1_4_8,2_8_7,1_6_5],[1_9_5,1_4_8,2_8_7,1_6_5],[2_9_5,1_4_8,3_4_9,1_6_5],[4_4_1,1_4_9,4_9_2,1_6_6],[4_9_7,1_4_9,5_4_6,1_6_4],[6_4,2_0_1,1_2_5,2_1_8],[1_0_0_0,1_0_0_0,1_0_0_0,1_0_0_0]],[[0,0,0,0],[6_6_2,1_5_0,7_5_4,1_6_6],[6_6_5,1_9_9,7_4_2,2_1_1],[5_1_9,2_1_3,5_5_4,2_2_8],[5_1_9,2_1_3,5_5_4,2_2_8],[1_3_4,4_3_3,1_8_7,4_5_4],[1_3_0,4_6_7,2_0_4,4_8_0],[1_3_0,4_6_7,2_0_4,4_8_0],[1_3_0,4_6_7,2_0_4,4_8_0],[1_3_0,4_6_7,2_0_4,4_8_0],[1_3_0,4_6_7,2_0_4,4_8_0],[3_1_4,4_6_9,3_7_6,4_8_2],[5_0_4,6_8_4,5_8_2,7_0_6],[9_4_1,8_2_5,9_7_3,9_0_0],[9_4_1,8_2_5,9_7_3,9_0_0],[9_4_1,8_2_5,9_7_3,9_0_0],[9_4_1,8_2_5,9_7_3,9_0_0],[6_1_0,7_4_9,6_5_2,7_6_5],[1_3_0,6_5_9,1_6_8,6_7_2],[1_7_6,6_5_7,2_3_7,6_7_2],[2_3_8,6_5_7,3_1_2,6_7_2],[4_4_3,6_5_3,6_2_8,6_7_2],[4_4_3,6_5_3,6_2_8,6_7_2],[7_1_6,3_0_1,8_2_5,3_1_7],[1_0_0_0,1_0_0_0,1_0_0_0,1_0_0_0]]]	)  # noqa: E231
	lowerCAmelCase_      							=			tf.convert_to_tensor([[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]]	)  # noqa: E231
	# these are sequence labels (i.e. at the token level)
	lowerCAmelCase_      							=			tf.convert_to_tensor([[-1_0_0,1_0,1_0,1_0,9,1,-1_0_0,7,7,-1_0_0,7,7,4,2,5,2,8,8,-1_0_0,-1_0_0,5,0,3,2,-1_0_0],[-1_0_0,1_2,1_2,1_2,-1_0_0,1_2,1_0,-1_0_0,-1_0_0,-1_0_0,-1_0_0,1_0,1_2,9,-1_0_0,-1_0_0,-1_0_0,1_0,1_0,1_0,9,1_2,-1_0_0,1_0,-1_0_0]]	)  # noqa: E231
	# fmt: on

	return input_ids, attention_mask, bbox, token_type_ids, labels







@require_tf
class    __a						( unittest.TestCase							):







				@slow
				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					lowerCAmelCase_      							=			TFLayoutLMModel.from_pretrained('''microsoft/layoutlm-base-uncased'''   )

					lowerCAmelCase_							,	lowerCAmelCase_							,	lowerCAmelCase_							,	lowerCAmelCase_							,	lowerCAmelCase_      							=			prepare_layoutlm_batch_inputs()

					# forward pass
					lowerCAmelCase_      							=			model(input_ids=UpperCAmelCase      ,	bbox=UpperCAmelCase      ,	attention_mask=UpperCAmelCase      ,	token_type_ids=UpperCAmelCase   )

					# test the sequence output on [0, :3, :3]
					lowerCAmelCase_      							=			tf.convert_to_tensor(
					    [[0.1_7_8_5, -0.1_9_4_7, -0.0_4_2_5], [-0.3_2_5_4, -0.2_8_0_7, 0.2_5_5_3], [-0.5_3_9_1, -0.3_3_2_2, 0.3_3_6_4]]      ,	)

					self.assertTrue(np.allclose(outputs.last_hidden_state[0, :3, :3]      ,	UpperCAmelCase      ,	atol=1E-3   )   )

					# test the pooled output on [1, :3]
					lowerCAmelCase_      							=			tf.convert_to_tensor([-0.6_5_8_0, -0.0_2_1_4, 0.8_5_5_2]   )

					self.assertTrue(np.allclose(outputs.pooler_output[1, :3]      ,	UpperCAmelCase      ,	atol=1E-3   )   )







				@slow
				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					lowerCAmelCase_      							=			TFLayoutLMForSequenceClassification.from_pretrained('''microsoft/layoutlm-base-uncased'''      ,	num_labels=2   )

					lowerCAmelCase_							,	lowerCAmelCase_							,	lowerCAmelCase_							,	lowerCAmelCase_							,	lowerCAmelCase_      							=			prepare_layoutlm_batch_inputs()

					# forward pass
					lowerCAmelCase_      							=			model(
					    input_ids=UpperCAmelCase      ,	bbox=UpperCAmelCase      ,	attention_mask=UpperCAmelCase      ,	token_type_ids=UpperCAmelCase      ,	labels=tf.convert_to_tensor([1, 1]   )      ,	)

					# test whether we get a loss as a scalar
					lowerCAmelCase_      							=			outputs.loss
					lowerCAmelCase_      							=			(2,)
					self.assertEqual(loss.shape      ,	UpperCAmelCase   )

					# test the shape of the logits
					lowerCAmelCase_      							=			outputs.logits
					lowerCAmelCase_      							=			(2, 2)
					self.assertEqual(logits.shape      ,	UpperCAmelCase   )







				@slow
				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					lowerCAmelCase_      							=			TFLayoutLMForTokenClassification.from_pretrained('''microsoft/layoutlm-base-uncased'''      ,	num_labels=13   )

					lowerCAmelCase_							,	lowerCAmelCase_							,	lowerCAmelCase_							,	lowerCAmelCase_							,	lowerCAmelCase_      							=			prepare_layoutlm_batch_inputs()

					# forward pass
					lowerCAmelCase_      							=			model(
					    input_ids=UpperCAmelCase      ,	bbox=UpperCAmelCase      ,	attention_mask=UpperCAmelCase      ,	token_type_ids=UpperCAmelCase      ,	labels=UpperCAmelCase   )

					# test the shape of the logits
					lowerCAmelCase_      							=			outputs.logits
					lowerCAmelCase_      							=			tf.convert_to_tensor((2, 25, 13)   )
					self.assertEqual(logits.shape      ,	UpperCAmelCase   )







				@slow
				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					lowerCAmelCase_      							=			TFLayoutLMForQuestionAnswering.from_pretrained('''microsoft/layoutlm-base-uncased'''   )

					lowerCAmelCase_							,	lowerCAmelCase_							,	lowerCAmelCase_							,	lowerCAmelCase_							,	lowerCAmelCase_      							=			prepare_layoutlm_batch_inputs()

					# forward pass
					lowerCAmelCase_      							=			model(input_ids=UpperCAmelCase      ,	bbox=UpperCAmelCase      ,	attention_mask=UpperCAmelCase      ,	token_type_ids=UpperCAmelCase   )

					# test the shape of the logits
					lowerCAmelCase_      							=			tf.convert_to_tensor((2, 25)   )
					self.assertEqual(outputs.start_logits.shape      ,	UpperCAmelCase   )
					self.assertEqual(outputs.end_logits.shape      ,	UpperCAmelCase   )



"""simple docstring"""



from google.protobuf import descriptor as _descriptor
from google.protobuf import descriptor_pool as _descriptor_pool
from google.protobuf import symbol_database as _symbol_database
from google.protobuf.internal import builder as _builder


# @@protoc_insertion_point(imports)

lowercase_							=							_symbol_database.Default()


lowercase_							=							_descriptor_pool.Default().AddSerializedFile(
    b'\n\x19sentencepiece_model.proto\x12\rsentencepiece"\x80\x0c\n\x0bTrainerSpec\x12\r\n\x05input\x18\x01 \x03(\t\x12\x14\n\x0cinput_format\x18\x07 \x01(\t\x12\x14\n\x0cmodel_prefix\x18\x02 \x01(\t\x12\x41\n\nmodel_type\x18\x03 \x01(\x0e\x32$.sentencepiece.TrainerSpec.ModelType:\x07UNIGRAM\x12\x18\n\nvocab_size\x18\x04 \x01(\x05:\x04\x38\x30\x30\x30\x12\x17\n\x0f\x61\x63\x63\x65pt_language\x18\x05 \x03(\t\x12 \n\x15self_test_sample_size\x18\x06 \x01(\x05:\x01\x30\x12*\n\x1b\x65nable_differential_privacy\x18\x32 \x01(\x08:\x05\x66\x61lse\x12+\n differential_privacy_noise_level\x18\x33 \x01(\x02:\x01\x30\x12\x32\n\'differential_privacy_clipping_threshold\x18\x34 \x01(\x04:\x01\x30\x12"\n\x12\x63haracter_coverage\x18\n \x01(\x02:\x06\x30.9995\x12\x1e\n\x13input_sentence_size\x18\x0b \x01(\x04:\x01\x30\x12$\n\x16shuffle_input_sentence\x18\x13 \x01(\x08:\x04true\x12 \n\x14mining_sentence_size\x18\x0c \x01(\x05\x42\x02\x18\x01\x12"\n\x16training_sentence_size\x18\r \x01(\x05\x42\x02\x18\x01\x12(\n\x17seed_sentencepiece_size\x18\x0e \x01(\x05:\x07\x31\x30\x30\x30\x30\x30\x30\x12\x1e\n\x10shrinking_factor\x18\x0f \x01(\x02:\x04\x30.75\x12!\n\x13max_sentence_length\x18\x12 \x01(\x05:\x04\x34\x31\x39\x32\x12\x17\n\x0bnum_threads\x18\x10 \x01(\x05:\x02\x31\x36\x12\x1d\n\x12num_sub_iterations\x18\x11 \x01(\x05:\x01\x32\x12$\n\x18max_sentencepiece_length\x18\x14 \x01(\x05:\x02\x31\x36\x12%\n\x17split_by_unicode_script\x18\x15 \x01(\x08:\x04true\x12\x1d\n\x0fsplit_by_number\x18\x17 \x01(\x08:\x04true\x12!\n\x13split_by_whitespace\x18\x16 \x01(\x08:\x04true\x12)\n\x1atreat_whitespace_as_suffix\x18\x18 \x01(\x08:\x05\x66\x61lse\x12+\n\x1c\x61llow_whitespace_only_pieces\x18\x1a \x01(\x08:\x05\x66\x61lse\x12\x1b\n\x0csplit_digits\x18\x19 \x01(\x08:\x05\x66\x61lse\x12#\n\x19pretokenization_delimiter\x18\x35 \x01(\t:\x00\x12\x17\n\x0f\x63ontrol_symbols\x18\x1e \x03(\t\x12\x1c\n\x14user_defined_symbols\x18\x1f \x03(\t\x12\x16\n\x0erequired_chars\x18$ \x01(\t\x12\x1c\n\rbyte_fallback\x18# \x01(\x08:\x05\x66\x61lse\x12+\n\x1dvocabulary_output_piece_score\x18  \x01(\x08:\x04true\x12\x1e\n\x10hard_vocab_limit\x18! \x01(\x08:\x04true\x12\x1c\n\ruse_all_vocab\x18" \x01(\x08:\x05\x66\x61lse\x12\x11\n\x06unk_id\x18( \x01(\x05:\x01\x30\x12\x11\n\x06\x62os_id\x18) \x01(\x05:\x01\x31\x12\x11\n\x06\x65os_id\x18* \x01(\x05:\x01\x32\x12\x12\n\x06pad_id\x18+ \x01(\x05:\x02-1\x12\x18\n\tunk_piece\x18- \x01(\t:\x05<unk>\x12\x16\n\tbos_piece\x18. \x01(\t:\x03<s>\x12\x17\n\teos_piece\x18/ \x01(\t:\x04</s>\x12\x18\n\tpad_piece\x18\x30 \x01(\t:\x05<pad>\x12\x1a\n\x0bunk_surface\x18, \x01(\t:\x05 \xe2\x81\x87 \x12+\n\x1ctrain_extremely_large_corpus\x18\x31 \x01(\x08:\x05\x66\x61lse"5\n\tModelType\x12\x0b\n\x07UNIGRAM\x10\x01\x12\x07\n\x03\x42PE\x10\x02\x12\x08\n\x04WORD\x10\x03\x12\x08\n\x04\x43HAR\x10\x04*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02"\xd1\x01\n\x0eNormalizerSpec\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x1c\n\x14precompiled_charsmap\x18\x02 \x01(\x0c\x12\x1e\n\x10\x61\x64\x64_dummy_prefix\x18\x03 \x01(\x08:\x04true\x12&\n\x18remove_extra_whitespaces\x18\x04 \x01(\x08:\x04true\x12 \n\x12\x65scape_whitespaces\x18\x05 \x01(\x08:\x04true\x12\x1e\n\x16normalization_rule_tsv\x18\x06 \x01(\t*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02"y\n\x0cSelfTestData\x12\x33\n\x07samples\x18\x01 \x03(\x0b\x32".sentencepiece.SelfTestData.Sample\x1a)\n\x06Sample\x12\r\n\x05input\x18\x01 \x01(\t\x12\x10\n\x08\x65xpected\x18\x02 \x01(\t*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02"\xfe\x03\n\nModelProto\x12\x37\n\x06pieces\x18\x01 \x03(\x0b\x32\'.sentencepiece.ModelProto.SentencePiece\x12\x30\n\x0ctrainer_spec\x18\x02 \x01(\x0b\x32\x1a.sentencepiece.TrainerSpec\x12\x36\n\x0fnormalizer_spec\x18\x03 \x01(\x0b\x32\x1d.sentencepiece.NormalizerSpec\x12\x33\n\x0eself_test_data\x18\x04 \x01(\x0b\x32\x1b.sentencepiece.SelfTestData\x12\x38\n\x11\x64\x65normalizer_spec\x18\x05 \x01(\x0b\x32\x1d.sentencepiece.NormalizerSpec\x1a\xd2\x01\n\rSentencePiece\x12\r\n\x05piece\x18\x01 \x01(\t\x12\r\n\x05score\x18\x02 \x01(\x02\x12\x42\n\x04type\x18\x03 \x01(\x0e\x32,.sentencepiece.ModelProto.SentencePiece.Type:\x06NORMAL"T\n\x04Type\x12\n\n\x06NORMAL\x10\x01\x12\x0b\n\x07UNKNOWN\x10\x02\x12\x0b\n\x07\x43ONTROL\x10\x03\x12\x10\n\x0cUSER_DEFINED\x10\x04\x12\x08\n\x04\x42YTE\x10\x06\x12\n\n\x06UNUSED\x10\x05*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02\x42\x02H\x03'
)

lowercase_							=							globals()
_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, _globals)
_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'sentencepiece_model_pb2', _globals)
if _descriptor._USE_C_DESCRIPTORS is False:
		lowercase_							=							None
		lowercase_							=							b'H\003'
		# (generated by protobuf compiler, but `_TRAINERSPEC` is not defined)
		# _TRAINERSPEC.fields_by_name["mining_sentence_size"]._options = None
		# _TRAINERSPEC.fields_by_name["mining_sentence_size"]._serialized_options = b"\030\001"
		# _TRAINERSPEC.fields_by_name["training_sentence_size"]._options = None
		# _TRAINERSPEC.fields_by_name["training_sentence_size"]._serialized_options = b"\030\001"
		lowercase_							=							45
		lowercase_							=							15_81
		lowercase_							=							15_17
		lowercase_							=							15_70
		lowercase_							=							15_84
		lowercase_							=							17_93
		lowercase_							=							17_95
		lowercase_							=							19_16
		lowercase_							=							18_64
		lowercase_							=							19_05
		lowercase_							=							19_19
		lowercase_							=							24_29
		lowercase_							=							22_08
		lowercase_							=							24_18
		lowercase_							=							23_23
		lowercase_							=							24_07
# @@protoc_insertion_point(module_scope)